Existing traffic signal control systems rely on oversimplified rule-based methods, and even RL-based methods are often suboptimal and unstable. To address this, we propose a cooperative multi-objective architecture called Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MOMA-DDPG), which estimates multiple reward terms for traffic signal control optimization using age-decaying weights. Our approach involves two types of agents: one focuses on optimizing local traffic at each intersection, while the other aims to optimize global traffic throughput. We evaluate our method using real-world traffic data collected from an Asian country's traffic cameras. Despite the inclusion of a global agent, our solution remains decentralized as this agent is no longer necessary during the inference stage. Our results demonstrate the effectiveness of MOMA-DDPG, outperforming state-of-the-art methods across all performance metrics. Additionally, our proposed system minimizes both waiting time and carbon emissions. Notably, this paper is the first to link carbon emissions and global agents in traffic signal control.

翻译：现有交通信号控制系统依赖于过度简化的基于规则的方法，即便是基于强化学习的方法也常常是次优且不稳定的。为解决这一问题，我们提出了一种协同多目标架构，称为多目标多智能体深度确定性策略梯度（MOMA-DDPG），该架构利用年龄衰减权重估计多个奖励项以优化交通信号控制。我们的方法涉及两类智能体：一类专注于优化每个交叉口的局部交通状况，另一类则旨在优化全局交通吞吐量。我们使用从某亚洲国家交通摄像头收集的真实世界交通数据评估所提方法。尽管包含全局智能体，我们的方案仍保持去中心化，因为该智能体在推理阶段不再必需。实验结果表明，MOMA-DDPG在所有性能指标上均优于当前最先进方法，展现了其有效性。此外，我们提出的系统同时最小化等待时间与碳排放。值得注意的是，本文首次将碳排放与全局智能体关联于交通信号控制领域。